Analysis of GLDS-147 from NASA GeneLab
This R markdown file was auto-generated by the iDEP website Using iDEP 0.91, originally by Steven Xijin.Ge@sdstate.edu
Ge SX, Son EW, Yao R: iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinformatics 2018, 19(1):534. PMID:30567491
First we set up the working directory to where the files are saved.
setwd('~/Documents/HTML_R/GLDS147')
R packages and iDEP core Functions. Users can also download the iDEP_core_functions.R file. Many R packages needs to be installed first. This may take hours. Each of these packages took years to develop.So be a patient thief. Sometimes dependencies needs to be installed manually. If you are using an older version of R, and having trouble with package installation, try un-install the current version of R, delete all folders and files (C:/Program Files/R/R-3.4.3), and reinstall from scratch.
if(file.exists('iDEP_core_functions.R'))
source('iDEP_core_functions.R') else
source('https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/iDEP_core_functions.R')
We are using the downloaded gene expression file where gene IDs has been converted to Ensembl gene IDs. This is because the ID conversion database is too large to download. You can use your original file if your file uses Ensembl ID, or you do not want to use the pathway files available in iDEP (or it is not available).
inputFile <- 'GLDS147_Expression.csv'
sampleInfoFile <- 'GLDS147_Sampleinfo.csv'
gldsMetadataFile <- 'GLDS147_Metadata.csv'
geneInfoFile <- 'Arabidopsis_thaliana__athaliana_eg_gene_GeneInfo.csv' #Gene symbols, location etc.
geneSetFile <- 'Arabidopsis_thaliana__athaliana_eg_gene.db' # pathway database in SQL; can be GMT format
STRING10_speciesFile <- 'https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/STRING10_species.csv'
Parameters for reading data
input_missingValue <- 'geneMedian' #Missing values imputation method
input_dataFileFormat <- 1 #1- read counts, 2 FKPM/RPKM or DNA microarray
input_minCounts <- 0.5 #Min counts
input_NminSamples <- 1 #Minimum number of samples
input_countsLogStart <- 4 #Pseudo count for log CPM
input_CountsTransform <- 1 #Methods for data transformation of counts. 1-EdgeR's logCPM 2-VST, 3-rlog
readMetadata.out <- readMetadata(gldsMetadataFile)
library(knitr) # install if needed. for showing tables with kable
library(kableExtra)
kable( readMetadata.out ) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| ARG1KO_FLT_Rep1 | ARG1KO_FLT_Rep2 | ARG1KO_FLT_Rep3 | ARG1KO_FLT_Rep4 | ARG1KO_GC_Rep1 | ARG1KO_GC_Rep2 | ARG1KO_GC_Rep3 | ARG1KO_GC_Rep4 | WT_FLT_Rep1 | WT_FLT_Rep2 | WT_FLT_Rep3 | WT_FLT_Rep4 | WT_FLT_Rep5 | WT_GC_Rep1 | WT_GC_Rep2 | WT_GC_Rep3 | WT_GC_Rep4 | WT_GC_Rep5 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Sample.LongId | Atha.Col.0.HypocotylCC.ARG1.KO.FLT.Rep1.147.Array | Atha.Col.0.HypocotylCC.ARG1.KO.FLT.Rep2.147.Array | Atha.Col.0.HypocotylCC.ARG1.KO.FLT.Rep3.147.Array | Atha.Col.0.HypocotylCC.ARG1.KO.FLT.Rep4.147.Array | Atha.Col.0.HypocotylCC.ARG1.KO.GC.Rep1.147.Array | Atha.Col.0.HypocotylCC.ARG1.KO.GC.Rep2.147.Array | Atha.Col.0.HypocotylCC.ARG1.KO.GC.Rep3.147.Array | Atha.Col.0.HypocotylCC.ARG1.KO.GC.Rep4.147.Array | Atha.Col.0.HypocotylCC.WT.FLT.Rep1.147.Array | Atha.Col.0.HypocotylCC.WT.FLT.Rep2.147.Array | Atha.Col.0.HypocotylCC.WT.FLT.Rep3.147.Array | Atha.Col.0.HypocotylCC.WT.FLT.Rep4.147.Array | Atha.Col.0.HypocotylCC.WT.FLT.Rep5.147.Array | Atha.Col.0.HypocotylCC.WT.GC.Rep1.147.Array | Atha.Col.0.HypocotylCC.WT.GC.Rep2.147.Array | Atha.Col.0.HypocotylCC.WT.GC.Rep3.147.Array | Atha.Col.0.HypocotylCC.WT.GC.Rep4.147.Array | Atha.Col.0.HypocotylCC.WT.GC.Rep5.147.Array |
| Sample.Id | Atha.Col.0.HypocotylCC.ARG1.KO.FLT.Rep1 | Atha.Col.0.HypocotylCC.ARG1.KO.FLT.Rep2 | Atha.Col.0.HypocotylCC.ARG1.KO.FLT.Rep3 | Atha.Col.0.HypocotylCC.ARG1.KO.FLT.Rep4 | Atha.Col.0.HypocotylCC.ARG1.KO.GC.Rep1 | Atha.Col.0.HypocotylCC.ARG1.KO.GC.Rep2 | Atha.Col.0.HypocotylCC.ARG1.KO.GC.Rep3 | Atha.Col.0.HypocotylCC.ARG1.KO.GC.Rep4 | Atha.Col.0.HypocotylCC.WT.FLT.Rep1 | Atha.Col.0.HypocotylCC.WT.FLT.Rep2 | Atha.Col.0.HypocotylCC.WT.FLT.Rep3 | Atha.Col.0.HypocotylCC.WT.FLT.Rep4 | Atha.Col.0.HypocotylCC.WT.FLT.Rep5 | Atha.Col.0.HypocotylCC.WT.GC.Rep1 | Atha.Col.0.HypocotylCC.WT.GC.Rep2 | Atha.Col.0.HypocotylCC.WT.GC.Rep3 | Atha.Col.0.HypocotylCC.WT.GC.Rep4 | Atha.Col.0.HypocotylCC.WT.GC.Rep5 |
| Sample.Name | Atha_Col-0_HypocotylCC_ARG1-KO_FLT_Rep1 | Atha_Col-0_HypocotylCC_ARG1-KO_FLT_Rep2 | Atha_Col-0_HypocotylCC_ARG1-KO_FLT_Rep3 | Atha_Col-0_HypocotylCC_ARG1-KO_FLT_Rep4 | Atha_Col-0_HypocotylCC_ARG1-KO_GC_Rep1 | Atha_Col-0_HypocotylCC_ARG1-KO_GC_Rep2 | Atha_Col-0_HypocotylCC_ARG1-KO_GC_Rep3 | Atha_Col-0_HypocotylCC_ARG1-KO_GC_Rep4 | Atha_Col-0_HypocotylCC_WT_FLT_Rep1 | Atha_Col-0_HypocotylCC_WT_FLT_Rep2 | Atha_Col-0_HypocotylCC_WT_FLT_Rep3 | Atha_Col-0_HypocotylCC_WT_FLT_Rep4 | Atha_Col-0_HypocotylCC_WT_FLT_Rep5 | Atha_Col-0_HypocotylCC_WT_GC_Rep1 | Atha_Col-0_HypocotylCC_WT_GC_Rep2 | Atha_Col-0_HypocotylCC_WT_GC_Rep3 | Atha_Col-0_HypocotylCC_WT_GC_Rep4 | Atha_Col-0_HypocotylCC_WT_GC_Rep5 |
| GLDS | 147 | 147 | 147 | 147 | 147 | 147 | 147 | 147 | 147 | 147 | 147 | 147 | 147 | 147 | 147 | 147 | 147 | 147 |
| Accession | GLDS-147 | GLDS-147 | GLDS-147 | GLDS-147 | GLDS-147 | GLDS-147 | GLDS-147 | GLDS-147 | GLDS-147 | GLDS-147 | GLDS-147 | GLDS-147 | GLDS-147 | GLDS-147 | GLDS-147 | GLDS-147 | GLDS-147 | GLDS-147 |
| Hardware | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC | BRIC |
| Tissue | Cell cultures | Cell cultures | Cell cultures | Cell cultures | Cell cultures | Cell cultures | Cell cultures | Cell cultures | Cell cultures | Cell cultures | Cell cultures | Cell cultures | Cell cultures | Cell cultures | Cell cultures | Cell cultures | Cell cultures | Cell cultures |
| Age | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days | 12 days |
| Organism | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana |
| Ecotype | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 |
| Genotype | arg1 | arg1 | arg1 | arg1 | arg1 | arg1 | arg1 | arg1 | arg1 | arg1 | arg1 | arg1 | arg1 | arg1 | arg1 | arg1 | arg1 | arg1 |
| Variety | Col-0 arg1 | Col-0 arg1 | Col-0 arg1 | Col-0 arg1 | Col-0 arg1 | Col-0 arg1 | Col-0 arg1 | Col-0 arg1 | Col-0 arg1 | Col-0 arg1 | Col-0 arg1 | Col-0 arg1 | Col-0 arg1 | Col-0 arg1 | Col-0 arg1 | Col-0 arg1 | Col-0 arg1 | Col-0 arg1 |
| Radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth |
| Gravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial |
| Developmental | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture | 12 day old cell culture |
| Time.series.or.Concentration.gradient | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point |
| Light | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark | Dark |
| Assay..RNAseq. | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling | Microarray Transcription Profiling |
| Temperature | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS | Ambient ISS |
| Treatment.type | Arg1 functions in the physiological adaptation of undifferentiated plant cells to spaceflight | Arg1 functions in the physiological adaptation of undifferentiated plant cells to spaceflight | Arg1 functions in the physiological adaptation of undifferentiated plant cells to spaceflight | Arg1 functions in the physiological adaptation of undifferentiated plant cells to spaceflight | Arg1 functions in the physiological adaptation of undifferentiated plant cells to spaceflight | Arg1 functions in the physiological adaptation of undifferentiated plant cells to spaceflight | Arg1 functions in the physiological adaptation of undifferentiated plant cells to spaceflight | Arg1 functions in the physiological adaptation of undifferentiated plant cells to spaceflight | Arg1 functions in the physiological adaptation of undifferentiated plant cells to spaceflight | Arg1 functions in the physiological adaptation of undifferentiated plant cells to spaceflight | Arg1 functions in the physiological adaptation of undifferentiated plant cells to spaceflight | Arg1 functions in the physiological adaptation of undifferentiated plant cells to spaceflight | Arg1 functions in the physiological adaptation of undifferentiated plant cells to spaceflight | Arg1 functions in the physiological adaptation of undifferentiated plant cells to spaceflight | Arg1 functions in the physiological adaptation of undifferentiated plant cells to spaceflight | Arg1 functions in the physiological adaptation of undifferentiated plant cells to spaceflight | Arg1 functions in the physiological adaptation of undifferentiated plant cells to spaceflight | Arg1 functions in the physiological adaptation of undifferentiated plant cells to spaceflight |
| Treatment.intensity | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x |
| Treament.timing | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x | x |
| Preservation.Method. | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater |
readData.out <- readData(inputFile)
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
kable( head(readData.out$data) ) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| ARG1KO_FLT_Rep1 | ARG1KO_FLT_Rep2 | ARG1KO_FLT_Rep3 | ARG1KO_FLT_Rep4 | ARG1KO_GC_Rep1 | ARG1KO_GC_Rep2 | ARG1KO_GC_Rep3 | ARG1KO_GC_Rep4 | WT_FLT_Rep1 | WT_FLT_Rep2 | WT_FLT_Rep3 | WT_FLT_Rep4 | WT_FLT_Rep5 | WT_GC_Rep1 | WT_GC_Rep2 | WT_GC_Rep3 | WT_GC_Rep4 | WT_GC_Rep5 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AT1G30700 | 3.459432 | 3.459432 | 3.169925 | 3.584963 | 3.169925 | 3.321928 | 3.169925 | 3.000000 | 2.807355 | 3.000000 | 3.000000 | 3.321928 | 2.807355 | 3.000000 | 3.000000 | 2.807355 | 2.807355 | 3.169925 |
| AT5G07870 | 2.807355 | 2.807355 | 3.000000 | 3.000000 | 3.321928 | 3.321928 | 3.321928 | 3.321928 | 2.807355 | 2.807355 | 2.807355 | 2.807355 | 2.807355 | 2.807355 | 2.807355 | 2.807355 | 2.807355 | 2.807355 |
| AT3G25250 | 3.169925 | 3.000000 | 3.321928 | 3.321928 | 3.169925 | 3.169925 | 3.584963 | 3.321928 | 2.807355 | 2.807355 | 3.000000 | 3.000000 | 3.000000 | 3.000000 | 2.807355 | 3.000000 | 2.807355 | 3.000000 |
| AT5G22570 | 3.000000 | 3.000000 | 3.169925 | 3.321928 | 3.321928 | 3.321928 | 3.321928 | 3.000000 | 3.000000 | 3.000000 | 2.807355 | 2.807355 | 2.584963 | 2.807355 | 2.807355 | 2.807355 | 2.807355 | 3.000000 |
| AT1G28480 | 3.169925 | 3.169925 | 3.459432 | 3.459432 | 3.321928 | 3.459432 | 3.459432 | 3.459432 | 3.000000 | 3.000000 | 3.000000 | 3.169925 | 3.169925 | 3.169925 | 3.000000 | 3.000000 | 3.000000 | 3.169925 |
| AT5G07860 | 2.584963 | 2.807355 | 3.000000 | 3.000000 | 3.321928 | 3.321928 | 3.321928 | 3.169925 | 2.807355 | 2.807355 | 2.807355 | 2.807355 | 2.807355 | 2.807355 | 2.807355 | 2.807355 | 2.807355 | 2.807355 |
readSampleInfo.out <- readSampleInfo(sampleInfoFile)
kable( readSampleInfo.out ) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Gravity | Variety | |
|---|---|---|
| ARG1KO_FLT_Rep1 | Microgravity | Col0 arg1 |
| ARG1KO_FLT_Rep2 | Microgravity | Col0 arg1 |
| ARG1KO_FLT_Rep3 | Microgravity | Col0 arg1 |
| ARG1KO_FLT_Rep4 | Microgravity | Col0 arg1 |
| ARG1KO_GC_Rep1 | Terrestrial | Col0 arg1 |
| ARG1KO_GC_Rep2 | Terrestrial | Col0 arg1 |
| ARG1KO_GC_Rep3 | Terrestrial | Col0 arg1 |
| ARG1KO_GC_Rep4 | Terrestrial | Col0 arg1 |
| WT_FLT_Rep1 | Microgravity | Col0 WT |
| WT_FLT_Rep2 | Microgravity | Col0 WT |
| WT_FLT_Rep3 | Microgravity | Col0 WT |
| WT_FLT_Rep4 | Microgravity | Col0 WT |
| WT_FLT_Rep5 | Microgravity | Col0 WT |
| WT_GC_Rep1 | Terrestrial | Col0 WT |
| WT_GC_Rep2 | Terrestrial | Col0 WT |
| WT_GC_Rep3 | Terrestrial | Col0 WT |
| WT_GC_Rep4 | Terrestrial | Col0 WT |
| WT_GC_Rep5 | Terrestrial | Col0 WT |
input_selectOrg ="NEW"
input_selectGO <- 'GOBP' #Gene set category
input_noIDConversion = TRUE
allGeneInfo.out <- geneInfo(geneInfoFile)
converted.out = NULL
convertedData.out <- convertedData()
nGenesFilter()
## [1] "16156 genes in 18 samples. 16156 genes passed filter.\n Original gene IDs used."
convertedCounts.out <- convertedCounts() # converted counts, just for compatibility
# Read counts per library
parDefault = par()
par(mar=c(12,4,2,2))
# barplot of total read counts
x <- readData.out$rawCounts
groups = as.factor( detectGroups(colnames(x ) ) )
if(nlevels(groups)<=1 | nlevels(groups) >20 )
col1 = 'green' else
col1 = rainbow(nlevels(groups))[ groups ]
barplot( colSums(x)/1e6,
col=col1,las=3, main="Total read counts (millions)")
readCountsBias() # detecting bias in sequencing depth
## [1] 0.5231528
## [1] 0.92608
## [1] 0.1243607
## [1] "No bias detected"
# Box plot
x = readData.out$data
boxplot(x, las = 2, col=col1,
ylab='Transformed expression levels',
main='Distribution of transformed data')
#Density plot
par(parDefault)
## Warning in par(parDefault): graphical parameter "cin" cannot be set
## Warning in par(parDefault): graphical parameter "cra" cannot be set
## Warning in par(parDefault): graphical parameter "csi" cannot be set
## Warning in par(parDefault): graphical parameter "cxy" cannot be set
## Warning in par(parDefault): graphical parameter "din" cannot be set
## Warning in par(parDefault): graphical parameter "page" cannot be set
densityPlot()
# Scatter plot of the first two samples
plot(x[,1:2],xlab=colnames(x)[1],ylab=colnames(x)[2],
main='Scatter plot of first two samples')
####plot gene or gene family
input_selectOrg ="BestMatch"
input_geneSearch <- 'HOXA' #Gene ID for searching
genePlot()
## NULL
input_useSD <- 'FALSE' #Use standard deviation instead of standard error in error bar?
geneBarPlotError()
## NULL
# hierarchical clustering tree
x <- readData.out$data
maxGene <- apply(x,1,max)
# remove bottom 25% lowly expressed genes, which inflate the PPC
x <- x[which(maxGene > quantile(maxGene)[1] ) ,]
plot(as.dendrogram(hclust2( dist2(t(x)))), ylab="1 - Pearson C.C.", type = "rectangle")
#Correlation matrix
input_labelPCC <- TRUE #Show correlation coefficient?
correlationMatrix()
# Parameters for heatmap
input_nGenes <- 1000 #Top genes for heatmap
input_geneCentering <- TRUE #centering genes ?
input_sampleCentering <- FALSE #Center by sample?
input_geneNormalize <- FALSE #Normalize by gene?
input_sampleNormalize <- FALSE #Normalize by sample?
input_noSampleClustering <- FALSE #Use original sample order
input_heatmapCutoff <- 4 #Remove outliers beyond number of SDs
input_distFunctions <- 1 #which distant funciton to use
input_hclustFunctions <- 1 #Linkage type
input_heatColors1 <- 1 #Colors
input_selectFactorsHeatmap <- 'Gravity' #Sample coloring factors
png('heatmap.png', width = 10, height = 15, units = 'in', res = 300)
staticHeatmap()
dev.off()
## png
## 2
[heatmap] (heatmap.png)
heatmapPlotly() # interactive heatmap using Plotly
input_nGenesKNN <- 2000 #Number of genes fro k-Means
input_nClusters <- 4 #Number of clusters
maxGeneClustering = 12000
input_kmeansNormalization <- 'geneMean' #Normalization
input_KmeansReRun <- 0 #Random seed
distributionSD() #Distribution of standard deviations
KmeansNclusters() #Number of clusters
Kmeans.out = Kmeans() #Running K-means
KmeansHeatmap() #Heatmap for k-Means
#Read gene sets for enrichment analysis
sqlite <- dbDriver('SQLite')
input_selectGO3 <- 'GOBP' #Gene set category
input_minSetSize <- 15 #Min gene set size
input_maxSetSize <- 2000 #Max gene set size
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO3,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
# Alternatively, users can use their own GMT files by
#GeneSets.out <- readGMTRobust('somefile.GMT')
results <- KmeansGO() #Enrichment analysis for k-Means clusters
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Cluster | adj.Pval | Genes | Pathways |
|---|---|---|---|
| A | 9.09e-26 | 99 | Response to abiotic stimulus |
| 1.60e-24 | 93 | Response to organic substance | |
| 2.88e-23 | 83 | Response to endogenous stimulus | |
| 1.51e-22 | 81 | Response to hormone | |
| 5.10e-22 | 72 | Response to external stimulus | |
| 8.58e-22 | 76 | Cellular response to chemical stimulus | |
| 8.97e-20 | 62 | Defense response | |
| 8.97e-20 | 57 | Response to external biotic stimulus | |
| 8.97e-20 | 57 | Response to other organism | |
| 1.57e-19 | 57 | Response to biotic stimulus | |
| B | 6.64e-35 | 150 | Response to abiotic stimulus |
| 6.96e-34 | 154 | Regulation of gene expression | |
| 1.51e-28 | 146 | Nucleobase-containing compound biosynthetic process | |
| 3.18e-28 | 145 | Multicellular organism development | |
| 8.47e-28 | 131 | Response to organic substance | |
| 1.76e-27 | 137 | Regulation of biosynthetic process | |
| 6.95e-27 | 132 | Regulation of macromolecule biosynthetic process | |
| 1.20e-26 | 128 | Regulation of RNA metabolic process | |
| 3.09e-26 | 130 | Regulation of cellular macromolecule biosynthetic process | |
| 5.79e-26 | 133 | Regulation of cellular biosynthetic process | |
| C | 2.92e-15 | 60 | Oxidation-reduction process |
| 5.57e-12 | 67 | Response to abiotic stimulus | |
| 3.63e-11 | 29 | Generation of precursor metabolites and energy | |
| 3.98e-08 | 41 | Response to acid chemical | |
| 3.98e-08 | 49 | Response to oxygen-containing compound | |
| 3.99e-08 | 19 | Photosynthesis | |
| 3.99e-08 | 14 | Cellular respiration | |
| 4.64e-08 | 17 | Electron transport chain | |
| 3.76e-07 | 14 | Energy derivation by oxidation of organic compounds | |
| 8.37e-07 | 44 | Cellular response to chemical stimulus | |
| D | 6.32e-23 | 84 | Response to abiotic stimulus |
| 2.29e-15 | 61 | Response to oxygen-containing compound | |
| 1.84e-12 | 63 | Response to organic substance | |
| 1.84e-12 | 42 | Response to inorganic substance | |
| 1.84e-12 | 54 | Cellular response to chemical stimulus | |
| 1.03e-11 | 46 | Response to acid chemical | |
| 8.61e-11 | 54 | Response to endogenous stimulus | |
| 1.37e-10 | 53 | Response to hormone | |
| 6.47e-10 | 31 | Response to osmotic stress | |
| 8.14e-10 | 34 | Response to lipid |
input_seedTSNE <- 0 #Random seed for t-SNE
input_colorGenes <- TRUE #Color genes in t-SNE plot?
tSNEgenePlot() #Plot genes using t-SNE
input_selectFactors <- 'Gravity' #Factor coded by color
input_selectFactors2 <- 'Variety' #Factor coded by shape
input_tsneSeed2 <- 0 #Random seed for t-SNE
#PCA, MDS and t-SNE plots
PCAplot()
MDSplot()
tSNEplot()
#Read gene sets for pathway analysis using PGSEA on principal components
input_selectGO6 <- 'GOBP'
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO6,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
PCApathway() # Run PGSEA analysis
## Warning: Package 'KEGG.db' is deprecated and will be removed from Bioconductor
## version 3.12
cat( PCA2factor() ) #The correlation between PCs with factors
##
## Correlation between Principal Components (PCs) with factors
## PC1 is correlated with Variety (p=1.93e-02).
input_CountsDEGMethod <- 3 #DESeq2= 3,limma-voom=2,limma-trend=1
input_limmaPval <- 0.1 #FDR cutoff
input_limmaFC <- 2 #Fold-change cutoff
input_selectModelComprions <- 'Gravity: Microgravity vs. Terrestrial' #Selected comparisons
input_selectFactorsModel <- 'Gravity' #Selected comparisons
input_selectInteractions <- NULL #Selected comparisons
input_selectBlockFactorsModel <- NULL #Selected comparisons
factorReferenceLevels.out <- c('Gravity:Terrestrial')
limma.out <- limma()
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
## Error in estimateDispersionsFit(object, fitType = fitType, quiet = quiet): all gene-wise dispersion estimates are within 2 orders of magnitude
## from the minimum value, and so the standard curve fitting techniques will not work.
## One can instead use the gene-wise estimates as final estimates:
## dds <- estimateDispersionsGeneEst(dds)
## dispersions(dds) <- mcols(dds)$dispGeneEst
## ...then continue with testing using nbinomWaldTest or nbinomLRT
DEG.data.out <- DEG.data()
## Error in DEG.data(): object 'limma.out' not found
limma.out$comparisons
## Error in eval(expr, envir, enclos): object 'limma.out' not found
input_selectComparisonsVenn = limma.out$comparisons[1:3] # use first three comparisons
## Error in eval(expr, envir, enclos): object 'limma.out' not found
input_UpDownRegulated <- FALSE #Split up and down regulated genes
vennPlot() # Venn diagram
## Error in vennPlot(): object 'limma.out' not found
sigGeneStats() # number of DEGs as figure
## Error in sigGeneStats(): object 'limma.out' not found
sigGeneStatsTable() # number of DEGs as table
## Error in sigGeneStatsTable(): object 'limma.out' not found
input_selectContrast = limma.out$comparisons[1] # use first comparisons
## Error in eval(expr, envir, enclos): object 'limma.out' not found
selectedHeatmap.data.out <- selectedHeatmap.data()
## Error in selectedHeatmap.data(): object 'limma.out' not found
selectedHeatmap() # heatmap for DEGs in selected comparison
## Error in selectedHeatmap(): object 'selectedHeatmap.data.out' not found
# Save gene lists and data into files
write.csv( selectedHeatmap.data()$genes, 'heatmap.data.csv')
## Error in selectedHeatmap.data(): object 'limma.out' not found
write.csv(DEG.data(),'DEG.data.csv' )
## Error in DEG.data(): object 'limma.out' not found
write(AllGeneListsGMT() ,'AllGeneListsGMT.gmt')
## Error in AllGeneListsGMT(): object 'limma.out' not found
input_selectGO2 <- 'GOBP' #Gene set category
geneListData.out <- geneListData()
## Error in geneListData(): object 'input_selectContrast' not found
volcanoPlot()
## Error in volcanoPlot(): object 'limma.out' not found
scatterPlot()
## Error in scatterPlot(): object 'limma.out' not found
MAplot()
## Error in MAplot(): object 'limma.out' not found
geneListGOTable.out <- geneListGOTable()
## Error in geneListGOTable(): object 'selectedHeatmap.data.out' not found
# Read pathway data again
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO2,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
input_removeRedudantSets <- TRUE #Remove highly redundant gene sets?
results <- geneListGO() #Enrichment analysis
## Error in geneListGO(): object 'geneListGOTable.out' not found
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Cluster | adj.Pval | Genes | Pathways |
|---|---|---|---|
| A | 9.09e-26 | 99 | Response to abiotic stimulus |
| 1.60e-24 | 93 | Response to organic substance | |
| 2.88e-23 | 83 | Response to endogenous stimulus | |
| 1.51e-22 | 81 | Response to hormone | |
| 5.10e-22 | 72 | Response to external stimulus | |
| 8.58e-22 | 76 | Cellular response to chemical stimulus | |
| 8.97e-20 | 62 | Defense response | |
| 8.97e-20 | 57 | Response to external biotic stimulus | |
| 8.97e-20 | 57 | Response to other organism | |
| 1.57e-19 | 57 | Response to biotic stimulus | |
| B | 6.64e-35 | 150 | Response to abiotic stimulus |
| 6.96e-34 | 154 | Regulation of gene expression | |
| 1.51e-28 | 146 | Nucleobase-containing compound biosynthetic process | |
| 3.18e-28 | 145 | Multicellular organism development | |
| 8.47e-28 | 131 | Response to organic substance | |
| 1.76e-27 | 137 | Regulation of biosynthetic process | |
| 6.95e-27 | 132 | Regulation of macromolecule biosynthetic process | |
| 1.20e-26 | 128 | Regulation of RNA metabolic process | |
| 3.09e-26 | 130 | Regulation of cellular macromolecule biosynthetic process | |
| 5.79e-26 | 133 | Regulation of cellular biosynthetic process | |
| C | 2.92e-15 | 60 | Oxidation-reduction process |
| 5.57e-12 | 67 | Response to abiotic stimulus | |
| 3.63e-11 | 29 | Generation of precursor metabolites and energy | |
| 3.98e-08 | 41 | Response to acid chemical | |
| 3.98e-08 | 49 | Response to oxygen-containing compound | |
| 3.99e-08 | 19 | Photosynthesis | |
| 3.99e-08 | 14 | Cellular respiration | |
| 4.64e-08 | 17 | Electron transport chain | |
| 3.76e-07 | 14 | Energy derivation by oxidation of organic compounds | |
| 8.37e-07 | 44 | Cellular response to chemical stimulus | |
| D | 6.32e-23 | 84 | Response to abiotic stimulus |
| 2.29e-15 | 61 | Response to oxygen-containing compound | |
| 1.84e-12 | 63 | Response to organic substance | |
| 1.84e-12 | 42 | Response to inorganic substance | |
| 1.84e-12 | 54 | Cellular response to chemical stimulus | |
| 1.03e-11 | 46 | Response to acid chemical | |
| 8.61e-11 | 54 | Response to endogenous stimulus | |
| 1.37e-10 | 53 | Response to hormone | |
| 6.47e-10 | 31 | Response to osmotic stress | |
| 8.14e-10 | 34 | Response to lipid |
STRING-db API access. We need to find the taxonomy id of your species, this used by STRING. First we try to guess the ID based on iDEP’s database. Users can also skip this step and assign NCBI taxonomy id directly by findTaxonomyID.out = 10090 # mouse 10090, human 9606 etc.
STRING10_species = read.csv(STRING10_speciesFile)
ix = grep('Arabidopsis thaliana', STRING10_species$official_name )
findTaxonomyID.out <- STRING10_species[ix,1] # find taxonomyID
findTaxonomyID.out
## [1] 3702
Enrichment analysis using STRING
STRINGdb_geneList.out <- STRINGdb_geneList() #convert gene lists
## Error in STRINGdb_geneList(): object 'geneListData.out' not found
input_STRINGdbGO <- 'Process' #'Process', 'Component', 'Function', 'KEGG', 'Pfam', 'InterPro'
results <- stringDB_GO_enrichmentData() # enrichment using STRING
## Error in stringDB_GO_enrichmentData(): object 'selectedHeatmap.data.out' not found
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Cluster | adj.Pval | Genes | Pathways |
|---|---|---|---|
| A | 9.09e-26 | 99 | Response to abiotic stimulus |
| 1.60e-24 | 93 | Response to organic substance | |
| 2.88e-23 | 83 | Response to endogenous stimulus | |
| 1.51e-22 | 81 | Response to hormone | |
| 5.10e-22 | 72 | Response to external stimulus | |
| 8.58e-22 | 76 | Cellular response to chemical stimulus | |
| 8.97e-20 | 62 | Defense response | |
| 8.97e-20 | 57 | Response to external biotic stimulus | |
| 8.97e-20 | 57 | Response to other organism | |
| 1.57e-19 | 57 | Response to biotic stimulus | |
| B | 6.64e-35 | 150 | Response to abiotic stimulus |
| 6.96e-34 | 154 | Regulation of gene expression | |
| 1.51e-28 | 146 | Nucleobase-containing compound biosynthetic process | |
| 3.18e-28 | 145 | Multicellular organism development | |
| 8.47e-28 | 131 | Response to organic substance | |
| 1.76e-27 | 137 | Regulation of biosynthetic process | |
| 6.95e-27 | 132 | Regulation of macromolecule biosynthetic process | |
| 1.20e-26 | 128 | Regulation of RNA metabolic process | |
| 3.09e-26 | 130 | Regulation of cellular macromolecule biosynthetic process | |
| 5.79e-26 | 133 | Regulation of cellular biosynthetic process | |
| C | 2.92e-15 | 60 | Oxidation-reduction process |
| 5.57e-12 | 67 | Response to abiotic stimulus | |
| 3.63e-11 | 29 | Generation of precursor metabolites and energy | |
| 3.98e-08 | 41 | Response to acid chemical | |
| 3.98e-08 | 49 | Response to oxygen-containing compound | |
| 3.99e-08 | 19 | Photosynthesis | |
| 3.99e-08 | 14 | Cellular respiration | |
| 4.64e-08 | 17 | Electron transport chain | |
| 3.76e-07 | 14 | Energy derivation by oxidation of organic compounds | |
| 8.37e-07 | 44 | Cellular response to chemical stimulus | |
| D | 6.32e-23 | 84 | Response to abiotic stimulus |
| 2.29e-15 | 61 | Response to oxygen-containing compound | |
| 1.84e-12 | 63 | Response to organic substance | |
| 1.84e-12 | 42 | Response to inorganic substance | |
| 1.84e-12 | 54 | Cellular response to chemical stimulus | |
| 1.03e-11 | 46 | Response to acid chemical | |
| 8.61e-11 | 54 | Response to endogenous stimulus | |
| 1.37e-10 | 53 | Response to hormone | |
| 6.47e-10 | 31 | Response to osmotic stress | |
| 8.14e-10 | 34 | Response to lipid |
PPI network retrieval and analysis
input_nGenesPPI <- 100 #Number of top genes for PPI retrieval and analysis
stringDB_network1(1) #Show PPI network
## Error in stringDB_network1(1): object 'STRINGdb_geneList.out' not found
Generating interactive PPI
write(stringDB_network_link(), 'PPI_results.html') # write results to html file
## Error in stringDB_network_link(): object 'STRINGdb_geneList.out' not found
browseURL('PPI_results.html') # open in browser
input_selectContrast1 = limma.out$comparisons[1]
## Error in eval(expr, envir, enclos): object 'limma.out' not found
#input_selectContrast1 = limma.out$comparisons[3] # manually set
input_selectGO <- 'GOBP' #Gene set category
#input_selectGO='custom' # if custom gmt file
input_minSetSize <- 15 #Min size for gene set
input_maxSetSize <- 2000 #Max size for gene set
# Read pathway data again
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
input_pathwayPvalCutoff <- 0.2 #FDR cutoff
input_nPathwayShow <- 30 #Top pathways to show
input_absoluteFold <- FALSE #Use absolute values of fold-change?
input_GenePvalCutoff <- 1 #FDR to remove genes
input_pathwayMethod = 1 # 1 GAGE
gagePathwayData.out <- gagePathwayData() # pathway analysis using GAGE
## Error in gagePathwayData(): object 'limma.out' not found
results <- gagePathwayData.out #Enrichment analysis for k-Means clusters
## Error in eval(expr, envir, enclos): object 'gagePathwayData.out' not found
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Cluster | adj.Pval | Genes | Pathways |
|---|---|---|---|
| A | 9.09e-26 | 99 | Response to abiotic stimulus |
| 1.60e-24 | 93 | Response to organic substance | |
| 2.88e-23 | 83 | Response to endogenous stimulus | |
| 1.51e-22 | 81 | Response to hormone | |
| 5.10e-22 | 72 | Response to external stimulus | |
| 8.58e-22 | 76 | Cellular response to chemical stimulus | |
| 8.97e-20 | 62 | Defense response | |
| 8.97e-20 | 57 | Response to external biotic stimulus | |
| 8.97e-20 | 57 | Response to other organism | |
| 1.57e-19 | 57 | Response to biotic stimulus | |
| B | 6.64e-35 | 150 | Response to abiotic stimulus |
| 6.96e-34 | 154 | Regulation of gene expression | |
| 1.51e-28 | 146 | Nucleobase-containing compound biosynthetic process | |
| 3.18e-28 | 145 | Multicellular organism development | |
| 8.47e-28 | 131 | Response to organic substance | |
| 1.76e-27 | 137 | Regulation of biosynthetic process | |
| 6.95e-27 | 132 | Regulation of macromolecule biosynthetic process | |
| 1.20e-26 | 128 | Regulation of RNA metabolic process | |
| 3.09e-26 | 130 | Regulation of cellular macromolecule biosynthetic process | |
| 5.79e-26 | 133 | Regulation of cellular biosynthetic process | |
| C | 2.92e-15 | 60 | Oxidation-reduction process |
| 5.57e-12 | 67 | Response to abiotic stimulus | |
| 3.63e-11 | 29 | Generation of precursor metabolites and energy | |
| 3.98e-08 | 41 | Response to acid chemical | |
| 3.98e-08 | 49 | Response to oxygen-containing compound | |
| 3.99e-08 | 19 | Photosynthesis | |
| 3.99e-08 | 14 | Cellular respiration | |
| 4.64e-08 | 17 | Electron transport chain | |
| 3.76e-07 | 14 | Energy derivation by oxidation of organic compounds | |
| 8.37e-07 | 44 | Cellular response to chemical stimulus | |
| D | 6.32e-23 | 84 | Response to abiotic stimulus |
| 2.29e-15 | 61 | Response to oxygen-containing compound | |
| 1.84e-12 | 63 | Response to organic substance | |
| 1.84e-12 | 42 | Response to inorganic substance | |
| 1.84e-12 | 54 | Cellular response to chemical stimulus | |
| 1.03e-11 | 46 | Response to acid chemical | |
| 8.61e-11 | 54 | Response to endogenous stimulus | |
| 1.37e-10 | 53 | Response to hormone | |
| 6.47e-10 | 31 | Response to osmotic stress | |
| 8.14e-10 | 34 | Response to lipid |
pathwayListData.out = pathwayListData()
## Error in pathwayListData(): object 'gagePathwayData.out' not found
enrichmentPlot(pathwayListData.out, 25 )
## Error in enrichmentPlot(pathwayListData.out, 25): object 'pathwayListData.out' not found
enrichmentNetwork(pathwayListData.out )
## Error in h(simpleError(msg, call)): error in evaluating the argument 'X' in selecting a method for function 'lapply': object 'pathwayListData.out' not found
enrichmentNetworkPlotly(pathwayListData.out)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'X' in selecting a method for function 'lapply': object 'pathwayListData.out' not found
input_pathwayMethod = 3 # 1 fgsea
fgseaPathwayData.out <- fgseaPathwayData() #Pathway analysis using fgsea
## Error in fgseaPathwayData(): object 'limma.out' not found
results <- fgseaPathwayData.out #Enrichment analysis for k-Means clusters
## Error in eval(expr, envir, enclos): object 'fgseaPathwayData.out' not found
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Cluster | adj.Pval | Genes | Pathways |
|---|---|---|---|
| A | 9.09e-26 | 99 | Response to abiotic stimulus |
| 1.60e-24 | 93 | Response to organic substance | |
| 2.88e-23 | 83 | Response to endogenous stimulus | |
| 1.51e-22 | 81 | Response to hormone | |
| 5.10e-22 | 72 | Response to external stimulus | |
| 8.58e-22 | 76 | Cellular response to chemical stimulus | |
| 8.97e-20 | 62 | Defense response | |
| 8.97e-20 | 57 | Response to external biotic stimulus | |
| 8.97e-20 | 57 | Response to other organism | |
| 1.57e-19 | 57 | Response to biotic stimulus | |
| B | 6.64e-35 | 150 | Response to abiotic stimulus |
| 6.96e-34 | 154 | Regulation of gene expression | |
| 1.51e-28 | 146 | Nucleobase-containing compound biosynthetic process | |
| 3.18e-28 | 145 | Multicellular organism development | |
| 8.47e-28 | 131 | Response to organic substance | |
| 1.76e-27 | 137 | Regulation of biosynthetic process | |
| 6.95e-27 | 132 | Regulation of macromolecule biosynthetic process | |
| 1.20e-26 | 128 | Regulation of RNA metabolic process | |
| 3.09e-26 | 130 | Regulation of cellular macromolecule biosynthetic process | |
| 5.79e-26 | 133 | Regulation of cellular biosynthetic process | |
| C | 2.92e-15 | 60 | Oxidation-reduction process |
| 5.57e-12 | 67 | Response to abiotic stimulus | |
| 3.63e-11 | 29 | Generation of precursor metabolites and energy | |
| 3.98e-08 | 41 | Response to acid chemical | |
| 3.98e-08 | 49 | Response to oxygen-containing compound | |
| 3.99e-08 | 19 | Photosynthesis | |
| 3.99e-08 | 14 | Cellular respiration | |
| 4.64e-08 | 17 | Electron transport chain | |
| 3.76e-07 | 14 | Energy derivation by oxidation of organic compounds | |
| 8.37e-07 | 44 | Cellular response to chemical stimulus | |
| D | 6.32e-23 | 84 | Response to abiotic stimulus |
| 2.29e-15 | 61 | Response to oxygen-containing compound | |
| 1.84e-12 | 63 | Response to organic substance | |
| 1.84e-12 | 42 | Response to inorganic substance | |
| 1.84e-12 | 54 | Cellular response to chemical stimulus | |
| 1.03e-11 | 46 | Response to acid chemical | |
| 8.61e-11 | 54 | Response to endogenous stimulus | |
| 1.37e-10 | 53 | Response to hormone | |
| 6.47e-10 | 31 | Response to osmotic stress | |
| 8.14e-10 | 34 | Response to lipid |
pathwayListData.out = pathwayListData()
## Error in pathwayListData(): object 'fgseaPathwayData.out' not found
enrichmentPlot(pathwayListData.out, 25 )
## Error in enrichmentPlot(pathwayListData.out, 25): object 'pathwayListData.out' not found
enrichmentNetwork(pathwayListData.out )
## Error in h(simpleError(msg, call)): error in evaluating the argument 'X' in selecting a method for function 'lapply': object 'pathwayListData.out' not found
enrichmentNetworkPlotly(pathwayListData.out)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'X' in selecting a method for function 'lapply': object 'pathwayListData.out' not found
PGSEAplot() # pathway analysis using PGSEA
## Error in PGSEAplot(): object 'input_selectContrast1' not found
input_selectContrast2 = limma.out$comparisons[1]
## Error in eval(expr, envir, enclos): object 'limma.out' not found
#input_selectContrast2 = limma.out$comparisons[3] # manually set
input_limmaPvalViz <- 0.1 #FDR to filter genes
input_limmaFCViz <- 2 #FDR to filter genes
genomePlotly() # shows fold-changes on the genome
## Error in genomePlotly(): object 'limma.out' not found
input_nGenesBiclust <- 1000 #Top genes for biclustering
input_biclustMethod <- 'BCCC()' #Method: 'BCCC', 'QUBIC', 'runibic' ...
biclustering.out = biclustering() # run analysis
input_selectBicluster <- 1 #select a cluster
biclustHeatmap() # heatmap for selected cluster
input_selectGO4 <- 'GOBP' #Gene set category
# Read pathway data again
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO4,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
results <- geneListBclustGO() #Enrichment analysis for k-Means clusters
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| adj.Pval | Genes | Pathways |
|---|---|---|
| 1.4e-64 | 222 | Response to abiotic stimulus |
| 9.3e-44 | 182 | Response to organic substance |
| 9.3e-44 | 161 | Response to oxygen-containing compound |
| 1.0e-40 | 159 | Response to hormone |
| 1.8e-40 | 160 | Response to endogenous stimulus |
| 1.1e-38 | 139 | Response to external stimulus |
| 3.9e-36 | 126 | Response to acid chemical |
| 1.4e-32 | 138 | Cellular response to chemical stimulus |
| 1.4e-31 | 126 | Multi-organism process |
| 3.9e-30 | 50 | Cellular response to decreased oxygen levels |
input_mySoftPower <- 5 #SoftPower to cutoff
input_nGenesNetwork <- 1000 #Number of top genes
input_minModuleSize <- 20 #Module size minimum
wgcna.out = wgcna() # run WGCNA
## Warning: executing %dopar% sequentially: no parallel backend registered
## Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1 1 0.2890 1.630 0.928 312.000 311.000 451.00
## 2 2 0.0627 -0.305 0.901 136.000 132.000 251.00
## 3 3 0.2860 -0.597 0.934 69.100 64.400 153.00
## 4 4 0.5440 -0.862 0.968 38.700 34.500 99.20
## 5 5 0.6740 -1.030 0.962 23.100 19.800 67.80
## 6 6 0.7430 -1.130 0.990 14.500 11.900 47.90
## 7 7 0.7820 -1.260 0.989 9.510 7.460 34.80
## 8 8 0.7950 -1.350 0.971 6.430 4.810 25.90
## 9 9 0.8270 -1.390 0.987 4.480 3.160 19.60
## 10 10 0.8390 -1.450 0.991 3.190 2.140 15.20
## 11 12 0.8580 -1.520 0.974 1.730 1.080 9.42
## 12 14 0.8330 -1.650 0.950 1.010 0.585 6.54
## 13 16 0.8880 -1.680 0.986 0.628 0.344 4.77
## 14 18 0.8870 -1.700 0.941 0.412 0.199 3.60
## 15 20 0.3070 -2.920 0.255 0.283 0.118 3.09
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
softPower() # soft power curve
modulePlot() # plot modules
listWGCNA.Modules.out = listWGCNA.Modules() #modules
input_selectGO5 <- 'GOBP' #Gene set category
# Read pathway data again
GeneSets.out <-readGeneSets( geneSetFile,
convertedData.out, input_selectGO5,input_selectOrg,
c(input_minSetSize, input_maxSetSize) )
input_selectWGCNA.Module <- 'Entire network' #Select a module
input_topGenesNetwork <- 10 #SoftPower to cutoff
input_edgeThreshold <- 0.4 #Number of top genes
moduleNetwork() # show network of top genes in selected module
## softConnectivity: FYI: connecitivty of genes with less than 6 valid samples will be returned as NA.
## ..calculating connectivities..
input_removeRedudantSets <- TRUE #Remove redundant gene sets
results <- networkModuleGO() #Enrichment analysis of selected module
results$adj.Pval <- format( results$adj.Pval,digits=3 )
kable( results, row.names=FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| adj.Pval | Genes | Pathways |
|---|---|---|
| 1.4e-64 | 222 | Response to abiotic stimulus |
| 9.3e-44 | 182 | Response to organic substance |
| 9.3e-44 | 161 | Response to oxygen-containing compound |
| 1.0e-40 | 159 | Response to hormone |
| 1.8e-40 | 160 | Response to endogenous stimulus |
| 1.1e-38 | 139 | Response to external stimulus |
| 3.9e-36 | 126 | Response to acid chemical |
| 1.4e-32 | 138 | Cellular response to chemical stimulus |
| 1.4e-31 | 126 | Multi-organism process |
| 3.9e-30 | 50 | Cellular response to decreased oxygen levels |